Modern data pipelines are under pressure. Datasets that once fit comfortably in memory now span hundreds of gigabytes, and the expectation of near-real-time analytics has never been higher. CPUs, despite decades of optimization, are reaching their practical limits for the kind of massively parallel numerical work that dominates data science today. Two technologies have emerged as the foundation of a new approach: Apache Arrow, a universal columnar in-memory data format, and RAPIDS, NVIDIA’s open-source suite of GPU-accelerated data science libraries. Together they represent a meaningful shift in how analytical workloads are designed and executed.
Why Columnar Memory Layouts Matter
Before getting into either technology, it helps to understand why the way data is arranged in memory has such a large impact on performance. Traditional row-oriented storage is intuitive: each row of a table is stored contiguously, making it easy to insert or retrieve a single record. But analytical queries rarely touch single records. They scan millions of rows, apply filters, compute aggregations, and join tables, and for these operations, row-oriented layouts force the CPU or GPU to load far more data than it actually needs.
Columnar layouts flip this arrangement. Values from the same column are stored contiguously, so a query that needs only three columns out of fifty reads only those three columns from memory. This dramatically reduces memory bandwidth consumption and plays directly into the strengths of SIMD (Single Instruction, Multiple Data) instructions on CPUs and the massively parallel architecture of GPUs, both of which thrive on operating over contiguous blocks of the same data type.
Apache Arrow is built on exactly this insight.
What Apache Arrow Actually Is
Apache Arrow, hosted by the Apache Software Foundation and first released in 2016, is not a database or a query engine. It is a specification: a standardized, language-independent definition of how columnar data should be laid out in memory, along with a set of libraries in more than a dozen languages (C++, Python, Java, Rust, Go, R, and others) that implement this specification.
The practical consequence of this design is interoperability without serialization. When two systems both understand the Arrow format natively, data can be passed between them as a pointer to a memory buffer rather than being serialized into bytes, transmitted, and then deserialized on the other end. This zero-copy data sharing eliminates one of the most stubborn bottlenecks in multi-system data pipelines.
Arrow’s Core Components
Arrow’s specification covers several distinct but related areas. The columnar in-memory format defines precisely how arrays of integers, floats, strings, nested types, and null values should be arranged in memory buffers, including alignment rules that benefit both CPU cache performance and GPU memory access patterns. The IPC (Inter-Process Communication) format provides a standardized way to serialize Arrow data for transmission between processes or across a network. Arrow Flight is a high-performance RPC protocol built on the IPC format, enabling fast bulk data transfer between services using gRPC as the transport layer.
One more recent addition is the Arrow C Device Data Interface, which extends the original C Data Interface to support non-CPU memory, specifically GPU memory. This matters because RAPIDS cuDF, the GPU DataFrame library, stores Arrow-formatted data in CUDA device memory. Without a device-aware interface, every exchange between a GPU library and any other Arrow-compatible tool would require copying data back to the CPU first. The C Device Data Interface allows libraries to pass GPU-resident Arrow buffers to each other directly, keeping data on the device for as long as possible.
Arrow’s Role in the Broader Ecosystem
The list of projects that use Arrow natively is extensive. DuckDB, one of the fastest analytical databases for in-process queries, integrates Arrow for efficient data exchange. Pandas, the dominant Python data manipulation library, has used Arrow as an optional backend and increasingly embraces it as a default. Polars, a newer DataFrame library written in Rust, is built on Arrow throughout. Apache Spark supports Arrow-based data transfer for Python UDFs, dramatically reducing the cost of Python-JVM data exchange. InfluxDB IOx uses Arrow as its primary in-memory format. Dremio’s distributed SQL engine is based on Arrow.
This breadth of adoption means that choosing Arrow is not a niche decision. It is increasingly the default assumption across the analytical data ecosystem.
What RAPIDS Is and How It Fits In
RAPIDS is an open-source suite of GPU-accelerated data science libraries created by NVIDIA and released in 2018. It sits on top of NVIDIA CUDA and uses Apache Arrow as its foundational data structure. The design philosophy of RAPIDS is straightforward: provide the same APIs that Python data scientists already know, such as those from pandas, scikit-learn, and NetworkX, but run them on a GPU rather than a CPU.
The RAPIDS suite consists of several distinct libraries, each targeting a different domain.
cuDF: GPU DataFrames
cuDF is the core DataFrame library in RAPIDS. It implements the Apache Arrow columnar memory format in CUDA device memory and exposes a pandas-compatible API for loading, filtering, joining, aggregating, and transforming tabular data. For data engineers accustomed to pandas, the mental model is almost identical; the underlying execution happens on the GPU.
cuDF includes a pandas accelerator mode that allows existing pandas code to run on a GPU with a single import-time instruction and no other changes. This zero-code-change approach has been a deliberate design goal: rather than asking teams to rewrite their data pipelines, RAPIDS aims to make GPU acceleration a drop-in upgrade for existing workflows. A similar integration exists for Polars, where cuDF provides an optional GPU execution engine that Polars can delegate to for certain operations.
Because cuDF stores data in the Arrow format internally, interoperability with other Arrow-compatible tools is direct. Moving data between cuDF and PyArrow, for example, can be done without a host-device copy in many cases, using the Arrow C Device Data Interface to pass GPU memory buffers between the two libraries.
cuML: Machine Learning on the GPU
cuML provides GPU-accelerated implementations of common machine learning algorithms, with an API designed to match scikit-learn as closely as possible. Clustering, dimensionality reduction, regression, classification, and nearest-neighbor search are all included. UMAP and HDBSCAN, two algorithms frequently used in unsupervised learning and NLP workflows, are among the implementations that have shown particularly strong speedups on GPU hardware.
The connection to Arrow is indirect but important. Because cuML operates on cuDF DataFrames, and because cuDF uses Arrow-formatted buffers internally, the entire feature engineering and model training pipeline can remain on the GPU from start to finish, avoiding the costly round-trips to system memory that would otherwise occur at every stage boundary.
cuGraph: Graph Analytics
cuGraph brings GPU acceleration to graph algorithms, providing implementations of PageRank, community detection, shortest-path algorithms, triangle counting, and more. It integrates with NetworkX, the most widely used Python library for graph analysis, through a backend mechanism that allows NetworkX users to accelerate their existing code with no changes.
Graph analytics is a particularly good fit for GPU acceleration because many graph algorithms involve irregular parallelism over large numbers of vertices and edges, a workload that benefits from the thousands of threads a modern GPU can run simultaneously.
RAPIDS Memory Manager (RMM)
An often-overlooked but critical piece of RAPIDS is the RAPIDS Memory Manager, a library that provides centralized, configurable memory allocation for all GPU-resident data used by the suite. RMM supports pool allocators that pre-allocate a chunk of GPU memory and then serve allocations from that pool, dramatically reducing the overhead of frequent small allocations that would otherwise hit the CUDA runtime repeatedly. It also supports managed memory and asynchronous allocation, giving developers fine-grained control over how GPU memory is used across a pipeline.
Arrow and RAPIDS Together: The Data Flow
The most practically important aspect of combining Arrow and RAPIDS is understanding how data moves through a pipeline that includes both technologies.
Data typically originates in files, such as Parquet, ORC, CSV, or JSON, stored on disk or in object storage. Arrow provides high-performance readers for these formats; RAPIDS cuDF provides its own GPU-accelerated readers that load data directly into GPU memory, bypassing the CPU staging step that a naive pipeline would include. Reading a large Parquet file with cuDF loads the decompressed, decoded columnar data directly onto the GPU, where subsequent transformations happen without any intermediate copies.
When data needs to move between cuDF and a CPU-resident system, such as a pandas DataFrame or a PyArrow table, the transfer uses the Arrow format as the common medium. Because both systems understand the same memory layout, conversion is efficient: the bytes do not need to be reinterpreted or restructured, only moved between device and host memory.
In pipeline stages where GPU acceleration is not beneficial or not available, data can flow back to the CPU as a PyArrow table and be processed by any Arrow-compatible tool, then returned to the GPU for subsequent stages. This ability to mix CPU and GPU stages within a single coherent data flow, using Arrow as the connective tissue, is what makes the combination genuinely practical for production pipelines rather than just benchmarks.
RAPIDS and Apache Spark
One of the highest-impact deployment scenarios for RAPIDS is integration with Apache Spark. Tens of thousands of organizations use Spark for large-scale data processing, and moving them to GPU acceleration has historically required significant pipeline rewrites. The RAPIDS Accelerator for Apache Spark addresses this by providing a plugin that integrates with Spark’s query planner.
When the plugin is installed, Spark’s Catalyst optimizer is extended to identify operations in a query plan that can be accelerated by the GPU. Those operations are transparently redirected to RAPIDS, which executes them using cuDF and returns results in a format Spark can continue processing. Operations that cannot be accelerated continue to run on the CPU. Importantly, no code changes to the Spark application are required.
The RAPIDS Accelerator for Apache Spark has reported speedups of up to five times for existing Spark 3.x jobs on equivalent hardware, driven primarily by the GPU’s ability to process columnar batches of data far more quickly than CPU-based row or columnar processing. A GPU-aware shuffle implementation using OpenUCX further reduces data transfer overhead between Spark nodes by leveraging NVLink and RDMA where available, keeping data on the GPU across shuffle boundaries where possible.
Practical Considerations for Adoption
Adopting RAPIDS and Arrow in a production environment involves several practical decisions beyond the technical architecture.
The most immediate constraint is hardware. RAPIDS requires NVIDIA GPUs and is not compatible with AMD or Intel GPUs in its core libraries, though community efforts exist to broaden this. The minimum viable GPU for RAPIDS is typically an NVIDIA GPU with at least 8 GB of device memory; for large-scale ETL and ML workloads, 40 GB or 80 GB GPUs are common in production deployments.
Python version and CUDA version compatibility require attention during installation. RAPIDS versioning tracks CUDA versions closely, and mismatches between the installed NVIDIA driver, CUDA toolkit, and RAPIDS libraries are a common source of setup friction. The RAPIDS installation guide provides a compatibility matrix, and conda is the recommended package manager for managing these dependencies reliably.
For teams not ready to own their own GPU infrastructure, RAPIDS is available through cloud providers on GPU instances (Amazon EMR, Google Cloud Dataproc, and Databricks all support the RAPIDS Accelerator for Spark), and cuDF comes pre-installed in Google Colab GPU environments.
Real-World Performance Characteristics
Performance gains from RAPIDS are not uniform across all workloads. Operations that are highly parallelizable and memory-bound, such as groupby aggregations, joins on large tables, sorting, and filtering over hundreds of millions of rows, show the most dramatic speedups. Operations that are inherently sequential, involve complex conditional logic, or operate on very small datasets may see little benefit and can even be slower on GPU than on CPU due to data transfer overhead.
This means that the value of RAPIDS is highest in pipelines with large data volumes, where the cost of moving data to the GPU is amortized across many subsequent operations. For small to medium datasets that fit comfortably in CPU cache, traditional CPU-based tools may remain the better choice. The practical guidance from the RAPIDS team is to keep data on the GPU for as many pipeline stages as possible and to use profiling to identify which stages see the largest absolute time savings.
Conclusion
Apache Arrow and RAPIDS address two different but deeply related problems. Arrow solves the data interchange problem: how do diverse systems exchange columnar data efficiently, without serialization overhead and without each pair of tools needing its own custom connector. RAPIDS solves the computational problem: how do data science workflows take advantage of the massive parallelism available in modern GPUs without requiring teams to abandon familiar tools and APIs.
Where they meet is in the architectural decision to use Arrow as the common in-memory representation across CPU and GPU boundaries. This shared foundation is what allows a data engineer to read a Parquet file into a cuDF DataFrame, filter and join it on the GPU, export the result as a PyArrow table for a CPU-based post-processing step, and then pass it to a downstream system like DuckDB or Polars, all without unnecessary copies or format conversions.
